high performance computing on graphics processing units: hgpu.org

hgpu.org » Linear Algebra

PLB-HeC: A Profile-based Load-Balancing algorithm for Heterogeneous CPU-GPU Clusters

Luis Sant'Ana, Raphael Camargo, Daniel Cordeiro

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Tags: Algorithms, Computer science, CUDA, GPU cluster, Heterogeneous systems, Linear Algebra, MPI, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, Tesla K20

July 13, 2015 by hgpu

High Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

Anton Akusok, Kaj-Mikael Bjork, Yoan Miche, Amaury Lendasse

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, OpenCL, Package, Python

July 6, 2015 by hgpu

Concurrent Solutions to Linear Systems using Hybrid CPU/GPU Nodes

Oluwapelumi Adenikinju, Julian Gilyard, Joshua Massey, Thomas Stitt

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Tags: Computer science, CUDA, Linear Algebra, nVidia, OpenGL, OpenMP

June 26, 2015 by hgpu

Characterizing Dataset Dependence for Sparse Matrix-Vector Multiplication on GPUs

N. Sedaghati, A. Ashari, L. N. Pouchet, S. Parthasarathy, P. Sadayappan

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Performance, Sparse matrix, Tesla K40

June 16, 2015 by hgpu

Blocks and Fuel: Frameworks for deep learning

Bart van Merrienboer, Dzmitry Bahdanau, Vincent Dumoulin, Dmitriy Serdyuk, David Warde-Farley, Jan Chorowski, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Linear Algebra, Machine learning, MPI, Neural networks, nVidia, Package, Python, Symbolic Computation, Visualization

June 5, 2015 by hgpu

Implementation and performance analysis of the AXPY, DOT, and SpMV functions on Intel Xeon Phi and NVIDIA Tesla using OpenCL

E. Coronado-Barrientos, G. Indalecio, A. Garcia-Loureiro

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Tags: Computer science, Intel Xeon Phi, Linear Algebra, nVidia, OpenCL, Sparse matrix, Tesla S2050

April 27, 2015 by hgpu

Algorithm 9xx: Sparse QR Factorization on the GPU

Sencer Nuri Yeralan, Timothy A. Davis, Sanjay Ranka

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Package, Sparse matrix, Tesla C2070

April 25, 2015 by hgpu

Batched Matrix Computations on Hardware Accelerators

Azzam Haidar, Tingxing Dong, Piotr Luszczek, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Factorization, Linear Algebra, nVidia, Tesla K40

April 12, 2015 by hgpu

Reduction of a Symmetrical Matrix to Tridiagonal Form on GPUs

Shuotian Chen

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Tesla C2050, Tesla M2090, Thesis

April 4, 2015 by hgpu

Massively Parallel Analysis of Similarity Matrices on Heterogeneous Hardware

Tobias Rawald, Mike Sips, Norbert Marwan, Ulf Leser

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Tags: ATI, ATI Radeon HD 7470, Computer science, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX 690, OpenCL

March 30, 2015 by hgpu

High Performance Computing for solving large sparse systems. Optical Diffraction Tomography as a case of study

Gloria Ortega Lopez

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Tags: Computer science, CUDA, Differential equations, FEM, Finite difference, Finite element method, GPU cluster, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Package, Partial differential equations, PDEs, Sparse matrix, Tesla C2050, Tesla M2070, Tesla M2090, Thesis, Tomography

March 30, 2015 by hgpu

Data driven scheduling approach for the multi-node multi-GPU Cholesky decomposition

Yuki Tsujita, Toshio Endo

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Tags: Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Task scheduling, Tesla K20

March 25, 2015 by hgpu

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

pyATF: The Auto-Tuning Framework (ATF) in Python

pyATF: Constraint-Based Auto-Tuning in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Checkpoint/Restore tool

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

KernelBench: Can LLMs Write GPU Kernels?

KernelBench: Can LLMs Write Efficient GPU Kernels?

An MLIR-based toolchain for AMD AI Engine-enabled devices

Seamless acceleration of Fortran intrinsics via AMD AI engines

Forecasting time series with constraints

See all packages

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high performance computing on graphics processing units: hgpu.org

PLB-HeC: A Profile-based Load-Balancing algorithm for Heterogeneous CPU-GPU Clusters

High Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

Concurrent Solutions to Linear Systems using Hybrid CPU/GPU Nodes

Characterizing Dataset Dependence for Sparse Matrix-Vector Multiplication on GPUs

Blocks and Fuel: Frameworks for deep learning

Implementation and performance analysis of the AXPY, DOT, and SpMV functions on Intel Xeon Phi and NVIDIA Tesla using OpenCL

Algorithm 9xx: Sparse QR Factorization on the GPU

Batched Matrix Computations on Hardware Accelerators

Reduction of a Symmetrical Matrix to Tridiagonal Form on GPUs

Massively Parallel Analysis of Similarity Matrices on Heterogeneous Hardware

High Performance Computing for solving large sparse systems. Optical Diffraction Tomography as a case of study

Data driven scheduling approach for the multi-node multi-GPU Cholesky decomposition

Recent source codes

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Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

pyATF: The Auto-Tuning Framework (ATF) in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Checkpoint/Restore tool

KernelBench: Can LLMs Write GPU Kernels?

An MLIR-based toolchain for AMD AI Engine-enabled devices

Forecasting time series with constraints

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